It helps if you translate the Stata commands. Not everybody is fluent
in those. It would even help more if you would enlight us about the
function you used to fit the model. Getting the marginal effects is
not that hard at all, but how depends a bit on the function you used
to estimate the model.
You can try
predict(your_model,type="terms",terms="the_term_you're_interested_in")
For exact information, look at the respective predict function, eg if
you use lme, do ?predict.lme
Be aware of the fact that R normally choses the correct predict
function without you having to specify it. predict() works for most
model objects. Yet, depending on the model eacht predict function can
have different options or different functionality. That information is
in the help files of the specific function.
Cheers
Joris
On Wed, Jun 9, 2010 at 11:28 AM, mike mick <saint-filth at hotmail.com>
wrote:>
> Dear all,
>
> I need to use R for one estimation, and i have readily available ?stata
command, but i need also the R version of the same command.
> the estimation in stata is as following:
> ? 1. Compute mean values of relevant variables
>
>
>
> . sum inno lnE lnM
>
>
>
> ? ?Variable | ? ? ? Obs ? ? ? ?Mean ? ?Std. Dev. ? ? ? Min ? ? ? ?Max
>
> -------------+--------------------------------------------------------
>
> ? ? ? ?inno | ? ?146574 ? ?.0880374 ? ?.2833503 ? ? ? ? ?0 ? ? ? ? ?1
>
> ? ? ? ? lnE | ? ?146353 ? ?.9256239 ? ?1.732912 ?-4.473922 ? 10.51298
>
> ? ? ? ? lnM | ? ?146209 ? ?4.281903 ? ?1.862192 ?-4.847253 ? 13.71969
>
>
>
> ? ? ? ?2. Estimate model
>
>
>
> . xi: xtreg lnLP lnC lnL lnE lnM eco inno eco_inno eco_lnE eco_lnM i.year,
fe i(stno)
>
> i.year ? ? ? ? ? ?_Iyear_1997-1999 ? ?(naturally coded; _Iyear_1997
omitted)
>
>
>
> Fixed-effects (within) regression ? ? ? ? ? ? ? Number of obs ? ? ?= ?
?146167
>
> Group variable (i): stno ? ? ? ? ? ? ? ? ? ? ? ?Number of groups ? = ? ?
48855
>
>
>
> R-sq: ?within ?= 0.9908 ? ? ? ? ? ? ? ? ? ? ? ? Obs per group: min = ? ? ?
? 1
>
> ? ? ? between = 0.9122 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?avg = ? ? ?
3.0
>
> ? ? ? overall = 0.9635 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?max = ? ? ? ?
3
>
>
>
> ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?F(11,97301) ? ? ? ?=
949024.29
>
> corr(u_i, Xb) ?= 0.2166 ? ? ? ? ? ? ? ? ? ? ? ? Prob > F ? ? ? ? ? = ?
?0.0000
>
>
>
>
------------------------------------------------------------------------------
>
> ? ? ? ?lnLP | ? ? ?Coef. ? Std. Err. ? ? ?t ? ?P>|t| ? ? [95% Conf.
Interval]
>
>
-------------+----------------------------------------------------------------
>
> ? ? ? ? lnC | ? .0304896 ? .0009509 ? ?32.06 ? 0.000 ? ? .0286258 ?
?.0323533
>
> ? ? ? ? lnL | ?-.9835998 ? .0006899 -1425.74 ? 0.000 ? ? -.984952 ?
-.9822476
>
> ? ? ? ? lnE | ? .0652658 ? .0009439 ? ?69.14 ? 0.000 ? ? .0634158 ?
?.0671159
>
> ? ? ? ? lnM | ? .6729931 ? .0012158 ? 553.53 ? 0.000 ? ? ? .67061 ?
?.6753761
>
> ? ? ? ? eco | ? .0610348 ? .0177048 ? ? 3.45 ? 0.001 ? ? .0263336 ? ?
.095736
>
> ? ? ? ?inno | ? .0173824 ? .0058224 ? ? 2.99 ? 0.003 ? ? .0059706 ?
?.0287943
>
> ? ?eco_inno | ? .0080325 ? .0110815 ? ? 0.72 ? 0.469 ? ?-.0136872 ?
?.0297522
>
> ? ? eco_lnE | ? .0276226 ? ?.004059 ? ? 6.81 ? 0.000 ? ? ?.019667 ?
?.0355781
>
> ? ? eco_lnM | ?-.0214237 ? .0039927 ? ?-5.37 ? 0.000 ? ?-.0292494 ?
-.0135981
>
> ?_Iyear_1998 | ?-.0317684 ? .0013978 ? -22.73 ? 0.000 ? ? -.034508 ?
-.0290287
>
> ?_Iyear_1999 | ?-.0647261 ? .0027674 ? -23.39 ? 0.000 ? ?-.0701501 ?
-.0593021
>
> ? ? ? _cons | ? 1.802112 ? ?.009304 ? 193.69 ? 0.000 ? ? 1.783876 ?
?1.820348
>
>
-------------+----------------------------------------------------------------
>
> ? ? sigma_u | ?.38142386
>
> ? ? sigma_e | ? .2173114
>
> ? ? ? ? rho | ?.75494455 ? (fraction of variance due to u_i)
>
>
------------------------------------------------------------------------------
>
> F test that all u_i=0: ? ? F(48854, 97301) = ? ? 3.30 ? ? ? ?Prob > F =
0.0000
>
>
>
> ? ? ? ?3. Compute marginal effect of eco at sample mean
>
>
>
> . nlcom
(_b[eco]+_b[inno]*.0880374+_b[eco_lnE]*.9256239+_b[eco_lnM]*4.281903)
>
>
>
> ? ? ? _nl_1:
?_b[eco]+_b[inno]*.0880374+_b[eco_lnE]*.9256239+_b[eco_lnM]*4.281903
>
>
>
>
------------------------------------------------------------------------------
>
> ? ? ? ?lnLP | ? ? ?Coef. ? Std. Err. ? ? ?t ? ?P>|t| ? ? [95% Conf.
Interval]
>
>
-------------+----------------------------------------------------------------
>
> ? ? ? _nl_1 | ?-.0036011 ? ?.008167 ? ?-0.44 ? 0.659 ? ?-.0196084 ?
?.0124061
>
>
------------------------------------------------------------------------------
>
>
>
> in fact i can find the mean of the variables ( step 1) and extimate the
model (step 2) but i couldnt find the equivalent of step 3 (compute marginal
effect of eco at sample mean). Can someone help me for this issue?
>
> Cheers!
>
>
> _________________________________________________________________
>
>
> ? ? ? ?[[alternative HTML version deleted]]
>
> ______________________________________________
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>
--
Joris Meys
Statistical consultant
Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control
tel : +32 9 264 59 87
Joris.Meys at Ugent.be
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